1. Field of the Invention
The invention concerns a method for generation of 3D x-ray image data of a subject.
2. Description of the Prior Art
X-ray imaging is used in many fields of technology and medicine in order to acquire information about the inside of a subject that is not visible from the outside. Particularly in medicine, patients as subjects are irradiated with x-rays. The patients are normally living people or animals. In addition to the production of conventional 2D x-ray images, the production of 3D image data sets is increasingly gaining importance in medicine.
3D image data sets are generated as reconstructions composed of a number of conventionally-acquired 2D x-ray images. Today both mobile and stationary C-arm systems are used for the creation of such 3D reconstructions or 3D image data sets.
In both systems a sequence of 2D x-ray images is mapped along a predetermined trajectory. For example, given a stationary C-arm angio system several hundred 2D x-ray images of a patient are acquired during a 180° orbital scan. In such an orbital scan, the x-ray source and the x-ray image receiver of the angio system isocentrically orbit the body region of interest on an orbital path.
Due to the geometric calibration of such x-ray systems, which calibration is for the most part implemented in “offline” operation (meaning, for example, directly after the production, thus not in regular operation), the position of every single acquired 2D x-ray image is known relative to the isocenter of the system. The viewing angle, distances and other geometry parameters so determined are entered into and stored in projection matrices belonging to the x-ray system.
A 3D reconstruction volume is calculated from the multiple of 2D x-ray images by suitable methods such as, for example, back-projection. The projection matrices of the system are used for this reconstruction.
In order to obtain a high-quality 3D reconstruction, initially high-quality 2D x-ray images are required that into the corresponding back-projection method. Decisive quality parameters for the 2D x-ray images are, for example, contrast resolution, spatial resolution and the presence of artifacts. A high contrast resolution allows various organs to be differentiated using their brightness in the x-ray image. Artifacts are, for example, pillow or barrel distortions of the image or stripe artifacts, for example caused by ribs or the like moving during the acquisition.
The quality of the contrast resolution is significantly determined by the noise in the 2D x-ray images. The causes of this noise are, among other things, the quantum noise of the x-ray radiation, scatter radiation, spectral sensitivities (triggered, for example, by the “beam hardening effect”) and the detector noise in the x-ray image receiver.
In order to obtain a high-quality back projection or a high-quality 3D image data set, it is therefore particularly important to implement an effective noise reduction in the 2D x-ray images.
Various methods are known for this purpose. Noise can be minimized, for example, by a higher x-ray dose. In practice this is avoided as much as possible in order to expose the patient with an optimally low x-ray dose, particularly given the number of 2D x-ray exposures to be generated.
Furthermore it is known to apply a number of filter methods for 2D images (in particular for 2D x-ray images) in this context. Examples for this are line or area filters, morphological operators, frequency filters, median filters, sigma-Lee filters.
For example, from K. Wiesent et al., “Enhanced 3D-Reconstruction Algorithm for C-Arm Systems Suitable for Interventional Procedures”, IEEE Trans. on Med. Im., V. 19, N. 5, May 2000, it is known to filter the individual 2D x-ray images together with the inverse point image transformation of the back-projection.
Moreover, filter algorithms are known which are implemented after 3D reconstruction has occurred, thus being implemented directly on the 3D image data set. In the field of MPR visualization there are, for example, filter methods such as thick-MPR or de-streaking.
The goal of any noise suppression is ideally the removal of the noise from the 2D or 3D x-ray image without destroying image information. The corresponding filter methods which are respectively applied to a single 2D x-ray image or to the 3D image data set ultimately lead to a more or less good noise reduction in the 3D x-ray image data.